US5832421A - Method for blade temperature estimation in a steam turbine - Google Patents

Method for blade temperature estimation in a steam turbine Download PDF

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Publication number
US5832421A
US5832421A US08/764,381 US76438196A US5832421A US 5832421 A US5832421 A US 5832421A US 76438196 A US76438196 A US 76438196A US 5832421 A US5832421 A US 5832421A
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Prior art keywords
values
blade temperature
data
ann
blade
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Expired - Lifetime
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US08/764,381
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English (en)
Inventor
Nugroho Iwan Santoso
Thomas Petsche
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Siemens Corp
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Siemens Corporate Research Inc
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Priority to US08/764,381 priority Critical patent/US5832421A/en
Assigned to SIEMENS CORPORATE RESEARCH, INC. reassignment SIEMENS CORPORATE RESEARCH, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: PETSCHE, THOMAS, SANTOSO, NUGROHO I.
Priority to PCT/US1997/022159 priority patent/WO1998026336A1/en
Priority to RU99115461/09A priority patent/RU2213997C2/ru
Priority to JP52680798A priority patent/JP4005143B2/ja
Priority to CN97180635A priority patent/CN1105950C/zh
Priority to DE69706563T priority patent/DE69706563T2/de
Priority to AT97951539T priority patent/ATE205310T1/de
Priority to ES97951539T priority patent/ES2167023T3/es
Priority to CZ0212099A priority patent/CZ300956B6/cs
Priority to KR10-1999-7005120A priority patent/KR100523382B1/ko
Priority to EP97951539A priority patent/EP0944866B1/en
Priority to PL97333952A priority patent/PL185983B1/pl
Publication of US5832421A publication Critical patent/US5832421A/en
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Assigned to SIEMENS CORPORATION reassignment SIEMENS CORPORATION MERGER (SEE DOCUMENT FOR DETAILS). Assignors: SIEMENS CORPORATE RESEARCH, INC.
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01K—STEAM ENGINE PLANTS; STEAM ACCUMULATORS; ENGINE PLANTS NOT OTHERWISE PROVIDED FOR; ENGINES USING SPECIAL WORKING FLUIDS OR CYCLES
    • F01K13/00—General layout or general methods of operation of complete plants
    • F01K13/02—Controlling, e.g. stopping or starting
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/903—Control
    • Y10S706/904—Manufacturing or machine, e.g. agricultural machinery, machine tool

Definitions

  • temperature measuring devices are installed at the respective stages of the HP and LP casings. These measurements provides an indication to the operator or supervising engineer in charge whenever the blade temperature exceeds its limit.
  • the need for blade temperature monitoring for smaller and older turbine, as well as a more practical and cost effective ways than installing temperature probes, has led to a need, herein recognized, for a practical system for estimating in real time and monitoring turbine blade temperature during operation.
  • the present invention is intended to be practiced preferrably with the application of a programmable computer.
  • a method for blade temperature estimation in a steam turbine utilizes measurement values including pressure and temperature at locations other than directly at the blades, principally at the input and output stages.
  • blade temperature is simulated by using a water/steam cycle analysis program as well as by directed experiments.
  • An artificial neural network (ANN) is trained by presenting the measurement values and the blade temp values. In a present exemplary embodiment, it is found that 4 values provide a satisfactory result. In one method the ANN is used directly to derive operating blade temp values.
  • a hybrid approach 5 measured values are utilized.
  • a subset of, for example, 4 parameter values is used for training the ANN and another subset of, for example, 3 values is used for performing a calculation for another intermediate parameter.
  • a blade temperature is calculated.
  • the user interface provides a real-time information display for a supervising engineer in charge of turbine operation so that critical parameter values and undesirable combinations of operating conditions are readily observed and deviations are made apparent so that corrective action can be initiated rapidly. While graph plots of parameters can be readily presented, such a format generally does not readily provide an overall picture of the state of the turbine with regard to the distribution and combination of temperature, pressure, steam wetness or superheat, and turbulence effects.
  • an overview of the operating situation is made more readily apparent by representing the operating expansion and compression processes by lines on a Mollier enthalpy/entropy chart.
  • real-time parameter values and parameter trends are also presented.
  • the supervising engineer can more quickly identify and correct undesirable and potentially troublesome operation conditions.
  • a system utilizes a hybrid ANN (artificial neural network)algorithmic based scheme for estimating the blade temperature from other measurements which are commonly available.
  • the commonly available measurement values are herein utilized.
  • the training data for the ANN includes both data generated by mathematical model and by experiment.
  • FIG. 1 shows a windage module architecture in accordance wit he invention
  • FIGS. 2a-2b show an artificial neural network based schemes for blade temperature estimation in accordance with the invention
  • FIG. 3 shows a training procedure for an artificial neural network in accordance with the invention
  • FIGS. 4a, 4b, 4c and 4d show graphical user interface structures applicable in conjunction with the invention.
  • FIGS. 5a-5j shows graphical interface views applicable in conjunction with the invention.
  • the windage modules for HP and LP turbines in accordance with the present invention will provide the operator with an estimation of the blade temperature at the respective turbine stages.
  • the interactive user interface herein disclosed displays the real-time value, a trend graph of these values, and the respective states within the Mollier diagram. Supervisory recommendation may be deduced from the estimation and other available measurement values.
  • On-line visualization of the expansion/compression lines is especially beneficial for other parts of the turbine which are subject to overheating, due, in the present particular case, to the windage phenomenon.
  • the LP turbine For heating steam turbines when the control valves, for example, in the cross over line for the two lower heaters are closed, the LP turbine requires cooling steam to hold within permissible limits the temperature rise caused by windage in the last stage. In this operating mode, the steam in the LP turbine absorbs energy resulting from the windage losses which predominate significantly within the last stages.
  • DIGEST is a modular monitoring system for power system plant developed by the KWU-FTP activity of Siemens Aktiengesellschaft, (Simens AG), a corporation of Germany. DIGEST features a modular system architecture which can be divided into six different levels which will be explain briefly below. The module components are written in C, with much flexibility in building any structure of choice.
  • the proposed windage module system architecture is shown in FIG. 1.
  • the first two levels are already available as part of DIGEST. Modifications were done to the administrative and data levels. Modifications in both the communication and data levels include parameter specification which is needed for requesting the module-specific data through the data bus, and for creating the data server and data base.
  • the main windage module development is done mainly at the action and presentation levels.
  • the six levels in the windage module are:
  • Communication level This level basically is the communication server 6 which manages the transfer of information between the network and the DEC (Digital Equipment Corporation) digital workstation machine(s).
  • the standard DEC module that handles the communication issue is called Omni-Server/DECnet PhaseV.
  • the processes within the DEC which manage the data transfer are indicated by DEC-S5, 8, and S5-DEC, 10.
  • DEC-S5 manages the data transfer from the adminstrative level to the S5
  • S5-DEC manages data transfer from the S5 to the adminstrative level.
  • Adimistration level An administration level of control handles the data request from the windage process control by propagating the request in the right format to a communication level, which is done by a telegram distributor module 12. It also manages the incoming data in a certain format and forwards the data back to the process control for storage. This is done by a telegram receiver module 14. Other functions include managing the buffer capacity (de-log),16, self checking process (watch-dog), 18, and several timers/clocks for interrupt purposes (time-control), 20. Self checking process is mainly to check the status of all processes within the system, and re-boot the system if necessary.
  • Action level controls the continuous background process and computation. These include the initiation of data request (sending RQTs), management of incoming data (RDTs), data storage, all computation processes, and storage of results. A more detail description of this level can be found in the next section. This level may also include the output management which test the validity of the computation result.
  • the results of the hybrid artificial neural network (ANN) estimator are always compared to the result of the analytical module. This verification is required to detect possible bad results which are usually caused by input values which are far away from all samples that had been presented during the ANN training period. Large discrepancies may indicate that further retraining is in order.
  • ANN artificial neural network
  • Data level handles all processes concerning data storage and access. It includes the data server 22 and data base 24. All access to the data base must be done through the data server 22. Once the data is stored in the right format into the database 24, it can be accessed easily by all levels.
  • Presentation level provides a graphical user interface which allow the users to view all the necessary information in several different fashions, that is, current values, trend diagram and Mollier diagram. It consist of the Windage Graphical User Inteface 26, Free Graphics 28, and shared memory 30 for storing the intermediate parameter values needed for the user interface.
  • the free graphics is an independent graphical tool for plotting any parameter values stored in the data base. This tool is developed as a part of the original DIGEST system.
  • the information is presented in several layers starting with the main windage screen which will mainly show the blade temperatures.
  • the subsequent layers will show the detail conditions for each turbine section. These layers will provide information on all parameter values which are relevant to the operator for making appropriate decisions concerning the turbine operation. Further detail on the process within this level is provided in the following sections. detail in the next section.
  • a development screen is optionally provided for accessing some internal module and system parameters or processes; however, principally because of security reasons, this feature may preferrably be omitted in an actual working version.
  • the monitoring process may not always be necessary to cycle at the same rate at all times; it should depend on the turbine operating conditions. Several scenarios can be pre-determined for each specific turbine. For example, no load, full load, and low load during slow shutdown, start-up, and load rejection.
  • the monitoring cycle should be adjusted automatically for different conditions, depending on their criticalities, and the respective display may be arranged to pop-up to assist the operator.
  • the windage module basically has two main processes, the background process and the interactive display process.
  • the background process is responsible for obtaining the necessary parameter values, calculating the blade temperature at a predefine rate, and recording the relevant information into the appropriate shared memory and data base.
  • the interactive display process will show the necessary or requested information graphically at any point of time. The process rate is limited by the minimum amount of time required before all measurements stabilize, and will vary based on the severity of the turbine condition. Operation near the critical blade temperature may require faster process rate.
  • the training sub-structure is responsible for producing the appropriate weights and parameters that will be used in the monitoring module. This process is done off-line and is not controllable through the GUI interface.
  • the network is trained using the simulated data obtained by computing the estimated temperature using the analytical means for the expected normal operating domain, and actual data obtain from field experiments. The experiments concentrate on generating data in specific low steam flow conditions, such as shutdowns, loss of loads, and start-ups. This arrangement is expected to be able to estimate the blade temperature for the entire turbine operating ranges.
  • Minimal inputs to the estimator are the real-time measurement values of the pressure of the main steam, temperature of the main steam, pressure of the third stage and exhaust pressure. Additional inputs can be optionally provided and evaluated.
  • the background process will obtain measurement data, calculate the blade temperature and other necessary values, and store those values in appropriate locations.
  • the process sequences are as follows:
  • Receive measurement data from data acquisition system Simatic 5 (Siemens PLC).
  • the request is propagated through the ethernet network, communicated using the S5-DEC protocol, and managed by the tele-capture within the admistrative level.
  • the list of the measurement parameters include:
  • T1 Steam temperature before blading (°C.)
  • Teh Exhaust temperature before reheater (°C.)
  • Tcb Bottom casing temperature (°C.),
  • Tcu Upper casing temperature (°C.)
  • Tci Inside casing temperature (°C.)
  • Preprocess incoming data into the desired format (interpreter). This process basically reads the incoming data string and reformat it to a standard ASCII format. Store data in the intermediate files for futher processing.
  • the estimator will calculate the blade temperature value using the measurement values.
  • the input measurment values used for estimating the blade temperature, at least for the HP turbine, are:
  • FIG. 2 (a) One approach directly estimates the blade temperature using a straightforward 3 layer ANN, FIG. 2 (a).
  • the second approach uses a hybrid technique, FIG. 2(b) by decomposition of the intermediate parameters, where:
  • T3 One intermediate parameter (T3) is calculated analytically using ##EQU1## where n 0 is a given constant related to a specific turbine size.
  • n Another intermediate constant (n) will be calculated by the trained ANN based on the current input values.
  • the blade temperature estimation and other measurement parameters are then stored in two different places: the Data Base and intermediate Shared Memory.
  • FIG. 3 shows the general traning process which applicable to the ANN-module either in the direct approach or the hybrid approach. The only difference is in the input-parameters as indicated in the background process. The process can be described as follows:
  • the first step is data construction which basically combines the data obtained from simulation using water/steam cycle analysis and data obtained from the experiments.
  • Such analysis is for example included in thermodynamics modules within the DIGEST system.
  • the water/steam cycle analysis is used inside the themodynamic module in the DIGEST system.
  • the DIGEST monitoring system is currently available in the market through SIEMENS AG.
  • the data is re- formatted such it matches the input format of the ANN.
  • the data is then reorganized by separating the data into two separate data files where one is used for training and validation purposes, and one for testing purposes. Although there is no certain rule for regrouping the available data, data should be reorganized such that all operating regions should be well represented. In accordance with the present exemplary embodiment, 80% of the available data is utilized for training and validation and the rest for testing.
  • the ANN structure is a standard multilayer, with 1 hidden layer.
  • the number of hidden units may vary from 4 to 10 without significant improvement in performance: a longer traning period is needed for larger number of hidden units, and it may run the risk of overfitting.
  • the training process is started.
  • the optimization algorithm used is a standard technique available in various optimization or Neural Network textbooks. See, for example, Hertz, A. Krogh, and R. G. Palmer, "Introduction to the theory of neural computation", A lecture notes volume in the Santa Fe Institute Studies in The Sciences of Complexity, Addison-Wesley Publishing Company, July 1991; and D. Rumelhart, J. L. McClelland, and the PDP Reseach Group, "Parallel distributed processing: Exploration in the mocrostructure of cognition, Volume 1: Foundations", MIT Press, Cambridge 1987.
  • the ANN parameters connection weights and unit's threshold values
  • the training parameters must be modified until a solution is obtained.
  • the graphical user interface will also able to show the turbine conditions within the steam behavior Mollier diagram.
  • This diagram also called a Mollier chart, entropy/enthalpy diagram, or a total heat/entropy diagram, serves as a scientist and a plant. Therefore, this on-line turbine condition visualization will better help a user in taking appropriate control actions.
  • GUI process must be initiated by the user. It will access values stored by the background process as required.
  • the GUI process follows the following steps (see the correponding illustration in FIG. 4).
  • the Windage Graphical User Interface Module can be initiated independently or from within DIGEST. This will automatically initiate the connection to the Shared Memory unit.
  • the shared memory unit is basically a routine which manages the access and transfer of data between the GUI and any process outside it which mainly includes a buffer.
  • the ⁇ turbine overview window ⁇ gives the current value of the blade temperature, as well as other information which may be important for the user to make any decision concerning the control of the turbine.
  • the Mollier diagram is generated based on the standard thermodynamic calculation available on any thermodynamic text book such as the afore-mentioned books.
  • a routine is herein used which will generate the background Mollier grid, and then overlay the expansion data which are calculated from the current measurement values on top of the grid.
  • such a routine is available from Siemens AG in VISUM, a user manual, Version 3, October 1992.
  • Mollier option interface provide ways to personalize the viewing parameters to the user preferences. It also provide temperature thresholding which allow the user to set a certain threshold for activating the warning label and sending an alarm signal to the operator.
  • the trend diagram allows the selection of up to ten parameters to be shown at the same time.
  • the maximum number of parameters that can be shown is essentially unlimited; however, any number larger than ten will cause difficulties in viewing the graph itself. It has the same feature as feature #2 in the Mollier diagram.
  • the exact value within a graph can be found by clicking on the desired point. The exact value will be displayed under the corresponding axis.
  • the user can further analyze the data by selecting the ⁇ FREE GRAPHICS ⁇ which will give the user access to the complete data base. This component is provided within the DIGEST system.
  • the GUI display process will access the necessary data from the Shared Memory, with the exception of the FREE GRAPHICS routines which will access data from the data base through the data server.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
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  • Testing And Monitoring For Control Systems (AREA)
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US08/764,381 1996-12-13 1996-12-13 Method for blade temperature estimation in a steam turbine Expired - Lifetime US5832421A (en)

Priority Applications (12)

Application Number Priority Date Filing Date Title
US08/764,381 US5832421A (en) 1996-12-13 1996-12-13 Method for blade temperature estimation in a steam turbine
AT97951539T ATE205310T1 (de) 1996-12-13 1997-12-05 Verfahren zur schaufeltemperaturschätzung in einer dampfturbine
CZ0212099A CZ300956B6 (cs) 1996-12-13 1997-12-05 Zpusob urcování teploty lopatek u parní turbíny
JP52680798A JP4005143B2 (ja) 1996-12-13 1997-12-05 蒸気タービンにおける羽根温度推定方法
CN97180635A CN1105950C (zh) 1996-12-13 1997-12-05 汽轮机叶片温度预测方法
DE69706563T DE69706563T2 (de) 1996-12-13 1997-12-05 Verfahren zur schaufeltemperaturschätzung in einer dampfturbine
PCT/US1997/022159 WO1998026336A1 (en) 1996-12-13 1997-12-05 A method for blade temperature estimation in a steam turbine
ES97951539T ES2167023T3 (es) 1996-12-13 1997-12-05 Un metodo para la estimacion de la temperatura de las paletas en una turbina de vapor.
RU99115461/09A RU2213997C2 (ru) 1996-12-13 1997-12-05 Способ оценки температуры лопаток в паровой турбине
KR10-1999-7005120A KR100523382B1 (ko) 1996-12-13 1997-12-05 스팀 터빈의 블레이드 온도 추정 방법
EP97951539A EP0944866B1 (en) 1996-12-13 1997-12-05 A method for blade temperature estimation in a steam turbine
PL97333952A PL185983B1 (pl) 1996-12-13 1997-12-05 System oceny temperatury łopatek w turbinie parowej

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US08/764,381 US5832421A (en) 1996-12-13 1996-12-13 Method for blade temperature estimation in a steam turbine

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EP (1) EP0944866B1 (cs)
JP (1) JP4005143B2 (cs)
KR (1) KR100523382B1 (cs)
CN (1) CN1105950C (cs)
AT (1) ATE205310T1 (cs)
CZ (1) CZ300956B6 (cs)
DE (1) DE69706563T2 (cs)
ES (1) ES2167023T3 (cs)
PL (1) PL185983B1 (cs)
RU (1) RU2213997C2 (cs)
WO (1) WO1998026336A1 (cs)

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